Research On Displacement Tracking Control Of Permanent Magnet Linear Synchronous Motor Based On Backstepping

Traditional rotary motors indirectly obtain linear motion through gears,belts,ball screws and other devices.However,the driving method involves many intermediate components,which makes the system suffer from elastic deformation,mechanical loss,friction and other problems.At the same time,this technology has been unable to meet people's requirements for machining accuracy.The permanent magnet linear synchronous motor(PMLSM)has the characteristics of simple structure,fast response,and precise positioning.It has been widely used in microelectronics production,industrial robots,aerospace and other fields.However,PMLSM is easily affected by uncertain factors such as parameter perturbation and load disturbance,which in turn affects the stable operation of the system and the accuracy of tracking control.In order to weaken the influence of uncertain factors in PMLSM on system performance,improve the tracking control accuracy of the system,and enhance the robust stability of the system,this paper mainly studies the following aspects of PMLSM displacement tracking control:Firstly,considering that the speed loop of the PMLSM system is susceptible to parameter perturbations and external load disturbances,a backstepping sliding mode control method based on nonlinear disturbance observer is proposed.In this method,a nonlinear disturbance observer is constructed to estimate the uncertainties of the system.The simulation results show that the proposed control method can effectively improve the dynamic and static performance and the robust stability of the system.Secondly,considering that the speed loop and current loop of the PMLSM system are simultaneously affected by parameter perturbation,load disturbance and unmodeled dynamics,a dynamic surface backstepping sliding mode control method based on extreme learning machine(ELM)neural network is proposed.This method constructs a nonlinear disturbance observer(NDO)and an ELM neural network to estimate and estimate the non-matching uncertainties and matching uncertainties of the system respectively,and introduces the output observations into the designed dynamic surface backstepping.Compensation is performed in the module displacement tracking controller;the artificial fish swarm-leapfrog algorithm is used to optimize the main parameters of the designed controller,which effectively improves the system's displacement tracking accuracy and response speed.Finally,in order to achieve PLMSM displacement tracking error convergence within a fixed time,a dynamic surface backstepping control method based on a fixed time convergence observer is proposed.This method constructs a fixed-time convergence observer to observe and estimate the matching uncertain and non-matching uncertain items of the system;the dynamic surface and backstepping control are combined to complete the design of the PMLSM displacement tracking controller,which effectively avoids the conventional backstepping the "differential explosion" problem in control simplifies the design process of the system controller and enables the system state to converge within a fixed time.